Course Outline

segmentGetting Started (Don't Skip This Part)

segmentStatistics and Data Science: A Modeling Approach

segmentPART I: EXPLORING VARIATION

segmentChapter 1  Welcome to Statistics: A Modeling Approach

segmentChapter 2  Understanding Data

segmentChapter 3  Examining Distributions

segmentChapter 4  Explaining Variation

segmentPART II: MODELING VARIATION

segmentChapter 5  A Simple Model

segmentChapter 6  Quantifying Error

segmentChapter 7  Adding an Explanatory Variable to the Model

7.11 Improving Models by Adding Parameters

segmentChapter 8  Models with a Quantitative Explanatory Variable

segmentFinishing Up (Don't Skip This Part!)

segmentResources
list High School / Statistics and Data Science I (AB)
7.11 Improving Models by Adding Parameters
You probably noticed in the previous section that the threeparameter Height3Group
model explained more variation than the Height2Group
model; that is, it reduced the unexplained error more than the Height2Group
model when compared with the empty model.
If we look at histograms and jitter plots for the twogroup model and the threegroup model (below) you can get a sense of why this is. By adding more categories for height we are able to reduce the error variation around the mean height for each group.
In general, the more parameters we add to a model the less leftover error there is after subtracting out the model. Because we have said, many times, that the goal of the statistician is to reduce error, this seems like a good thing. And it is, but only to a point.
Let’s do a little thought experiment. You know already that the threegroup model explained more variation than the twogroup model. The fourgroup model would explain more than the threegroup. And so on. What would happen if we kept splitting into more groups until each person was in their own group?
If each person were in their own group, the error would be reduced to 0. Why? Because each person would have their own parameter in the model. If each person had their own parameter, then the predicted score for that person would just be the person’s actual score. And there would be no residual between the predicted and actual score.
There are two problems with this. First, even though the model fits our data perfectly, it would not fit if we were to choose another sample. Second, the purpose of creating a more complex model is to help us understand the Data Generating Process. It’s okay to add a little complexity to a model if it leads to better understanding. But if we have as many parameters as we have people, we have added a lot of complexity without contributing to our understanding of the DGP.
Although we can improve model fit by adding parameters to a model, there is always a tradeoff involved between reducing error (by adding more parameters to a model), on one hand, and increasing the intelligibility, simplicity, and elegance of a model, on the other.
This is a limitation of PRE as a measure of our success. If we get a PRE of .40, for example, that would be quite an accomplishment if we had only added a single parameter to the model. But if we had achieved that level by adding 10 parameters to the model, well, it’s just not as impressive. Statisticians sometimes call this “overfitting.”
There is a quote attributed to Einstein that sums up things pretty well: “Everything should be made as simple as possible, but not simpler.” A certain amount of complexity is required in our models just because of complexity in the world. But if we can simplify our model so as to help us make sense of complexity, and make predictions that are “good enough,” that is a good thing.